{"id":"W3195954751","doi":"10.3390/pr9091514","title":"Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing","year":2021,"lang":"en","type":"article","venue":"Processes","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Taif University","keywords":"Job shop scheduling; Computer science; Distributed computing; Fair-share scheduling; Dynamic priority scheduling; Scheduling (production processes); Rate-monotonic scheduling; Fixed-priority pre-emptive scheduling; Two-level scheduling; Cloud computing; Earliest deadline first scheduling; Round-robin scheduling; Flow shop scheduling; Real-time computing; Mathematical optimization; Computer network; Mathematics; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004961393,0.0006549222,0.0006143504,0.0006853865,0.0006546695,0.000601162,0.001048033,0.0004340693,0.001671776],"category_scores_gemma":[0.0007103755,0.0002040906,0.0005324528,0.0009234406,0.000296622,0.0006148363,0.00069255,0.0006041169,0.0003680727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006783369,"about_ca_system_score_gemma":0.001538964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00591702,"about_ca_topic_score_gemma":0.007582428,"domain_scores_codex":[0.9995204,0.0001034424,0.0000235158,0.00007972488,0.0001800005,0.0000928874],"domain_scores_gemma":[0.999791,0.0000438569,0.00003590282,0.00002118493,0.00006942945,0.00003866034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004498175,0.0002791236,0.001625704,0.0002860689,0.0001199793,0.0002501275,0.0002076606,0.5687423,0.03227752,0.01547738,0.01153969,0.3687446],"study_design_scores_gemma":[0.00004140218,0.0001203604,0.000438226,0.0000118364,0.00001589549,0.00007745708,0.00003951664,0.9865429,0.001684371,0.0047964,0.006215476,0.00001617185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02824544,0.000907876,0.9624709,0.0004192049,0.0002479934,0.0002519393,0.0001531509,0.001363619,0.005939781],"genre_scores_gemma":[0.6348197,0.0006655197,0.358844,0.0003024022,0.0001832394,0.0003253421,0.0003558432,0.0001864465,0.0043175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00591702,"threshold_uncertainty_score":0.01176512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645595706510644,"score_gpt":0.2586747537796179,"score_spread":0.2322187967145115,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}